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SAS Forum India: Big Data, Big Analytics & Bad Behaviour - Fighting Financial Crime
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SAS Forum India: Big Data, Big Analytics & Bad Behaviour - Fighting Financial Crime

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An exclusive presentation by Keith Swanson, Director, Financial Crimes, SAS South Asia presented on Big Data, Big Analytics & Bad Behaviour - Fighting Financial Crime.

An exclusive presentation by Keith Swanson, Director, Financial Crimes, SAS South Asia presented on Big Data, Big Analytics & Bad Behaviour - Fighting Financial Crime.

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  • 1. Big Data, Big Analytics &Bad Behaviour – FightingFinancial CrimeKeith SwansonDirector, Financial Crimes, SAS South Asia C o p y r i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r e s e r v e d .
  • 2. We hear much of the challenges that Big Data provides… …Volume, Velocity, Variety… …But how does Big Data impact our effort to combat Financial Crime? Adhoc & Regular More Formats Sources Volume Structured & Vision Unstructured BIG Velocity Combatting Veracity Financial DATA Crime More Sources/ Variety Channels Value Faster Data Internet of ThingsSimply put, you are likely being tasked with receiving more, doing more, and doing it for less Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 3. Financial Crime is pervasive across industries and shares many common means, opportunities and motives Financial Services Government Utilities FraudFinancial Crime Waste, Abuse Banking Taxation/Revenue Telecommunications Wealth & Investments Insurance Social Services Subscription Services (TV, etc.) AML/CTF FATCA Electric, Water, Gas Compliance Compliance Heat Health Insurance Internal & Procurement Fraud Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 4. SO, WHAT DOES THE INTERNET SAY BIG DATA LOOKS MAKING ‘CENTS’ OF LIKE? IF I WERE TO BELIEVE WHAT I SEE BIG DATA (BING.COM IMAGE SEARCH ON ‘BIG DATA’…) should we infer that source of light & big data may crush direction through us? stormy times? slow and archaickey to unlocking in dealing with something? it? something that will bite us? have strong belief and be shelter from inspired? perceived chaos? Big Data is not about what it looks like… …It is about what you make of it Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 5. MAKING ‘CENTS’ OF IN COMBATTING FINANCIAL CRIME, THE VARYING BIG DATA NATURE OF BIG DATA SHOULD BE ADDRESSED UNSTRUCTURED In motion and at DATA rest SYSTEM Fast and slowly DATA changing Big DataMultiple views of TRANSACTIONAL the truth DATA Central and INTERACTION distributed DATA Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 6. BIG DATA IN MORE FINANCIAL CRIME FOUND BY LINKING ACROSS THEFINANCIAL CRIME ENTERPRISE - WHICH ALSO CREATES MORE DATA Across Channels and Products Anti-Money Broker Creditd Debitd Wire Cheque ATM Phone Online Mobile Sanctions Loans Internal Laundering Surveillance Across Brands and Business Lines Personal/ SME/Business Wealth Insurance Affiliates Distribution Retail Across Relationship Levels & Types Customer Company Account Network Relationship Employee Across Transaction types Monetary Non-Monetary Inquiry Breed Success! - It does not have to be a ‘big bang’ approach. Start with the areas of highest losses, exposure and/or greatest ease of executionCopyright © 2013 , SAS Institute Inc. All rights reserved.
  • 7. GLOBAL COMBATTING FINANCIAL CRIME – ANALYSTS RECOMMEND A PERSPECTIVES – LAYERED APPROACH THAT DICTATES DEALING WITH BIGENTERPRISE FRAUD DATA MANAGEMENT LAYER 5 Entity Link Analysis:“There are two classes E Enables Analysis of Relationshipsof EFM solutions —one detects fraudulent F LAYER 4 Cross Channel Centric:transactions or Aunauthorized activities Monitors Entity Behavior Across Channels M Nas they occur, and A LAYER 3one detects organized L Channel Centric:crime and collusive Y Monitors Account Behavior for a Channelactivities using offlineentity link analysis” T I LAYER 2 Navigation Centric:- Avivah Latan, Gartner C Analyzes Session Behavior S LAYER 1 SAS assessed as 1 of Endpoint Centric: only 2 vendors who Authentication, Device ID, Geo Location do layers 4 and 5 Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 8. COMPLEXITIES OF THE UNDERLYING VALUE OF UNDERSTANDING BIG DATA – FINANCIAL CRIME UNDERSTANDING BEHAVIOUR Account Application Internal Transaction First Party Insurance Card Takeover/ ID Fraud Fraud Fraud Fraud Fraud Skimming Theft Man in the …And Brokerage/ Bust-out Procurement Multi-party Structured Browser/ many Trading Fraud Fraud Fraud Payments Middle more! Fraud Attack And inherent in people is their Behaviour PEOPLE! “The fraudulent act is a behavior that can be recognizable through advanced modeling techniques because we can anticipate that the behavior is sufficiently inconsistent with known normal behavior.” – John Geurts, Chief Security Officer, CBA So…. Stop Looking Just for Fraud, Look for Changes in Behaviour!Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 9. ANALYTICS IN INCREASING VALUE OF USING ANALYTICS TO ASSESS COMBATTING BIG DATA IN COMBATTING FINANCIAL CRIME FINANCIAL CRIME LOT Social Little Network Analysis Models, Advanced Fraud Detected Addresses Analytics False Positives the Knowns Anomaly Detection Addresses the Business Unknowns Rules Traditional Query and Little Analysis LOT Limited Approaches Applied Robust Low Maturity HighSIMPLY PUT, USING ANALYTICS FINDS MORE FRAUD AMONG BIG DATA. LOWER FALSE POSITIVES. IMPROVED PRODUCTIVITY. ANALYTICS FINDS THE EMERGING AND THE UNKNOWN Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 10. ANALYTICS IN USING A HYBRID APPROACH FOR DRIVING INSIGHTS COMBATTING FROM BIG DATA FINANCIAL CRIME Text Social Mining Network Predictive Analysis Modeling Anomaly Detection Automated Alert Business Rules Generation Process Database Searches and Watch Lists LEVERAGING SAS HYBRID APPROACH TO RISK ASSESS ACROSS MULTIPLE ORGANIZATIONSCopyright © 2013 , SAS Institute Inc. All rights reserved.
  • 11. ANALYTICS IN COMBATTING THE NEED FOR MULTIPLE ANALYTICAL MODELS FINANCIAL CRIMECopyright © 2013 , SAS Institute Inc. All rights reserved.
  • 12. ANALYTICS IN COMBATTING USING BIG ANALYTICS TO MAKE SENSE OF BIG DATA FINANCIAL CRIME Vision Vision VeracityApproach Predict Behaviour Surface Hidden Relationships Higher Quality Alerts Models look at the Use machine learning and Determining what is real behaviour of many to horsepower to identify and and what is false much predict how individuals visualise relationships – overt easier when looking at a may act and covert transaction in relation to • 15-30% Higher fraud value • 32x more fraud rings than • Detection accuracy detection rate previous approach improved by over 25xValue • 25% better performance • Automated analysis & the • 47% better detection than rules alone identification/visualisation of networks across millions Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 13. ANALYTICS IN COMBATTING USING BIG ANALYTICS TO MAKE SENSE OF BIG DATA FINANCIAL CRIME Vision Vision VeracityApproach Look at the Full Picture Address Knowns and Decisioning and Alerting Understanding financial & Unknowns Operationalise Analytics - non-financial transactions Multiple Analytic techniques Let the system find what is adds big data, but helps target the known and important and when it is give full view of behavior surface the unknown critical pushing ‘needles out of a haystack’ Over 1 Billion Transactions • 35% Better than the 100% Real time in analysed a day competitor, 57% Better than milliseconds –Value previous benchmarked to 3200+ • 8X ROI in the first 12 Transaction per second months Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 14. ANALYTICS IN BIG ANALYTICS CAN PROVIDE INSIGHTS FROM BIG DATA COMBATTING AT DIFFERENT LEVELS FINANCIAL CRIME VISION, VERACITY AND VALUE Enterprise/Population Targeted Information Proactive Alerting & Based Analysis Inquiries Decisioning Looking across an identity and Using the identity and network Proactive alerting of network view to qualify and view to query on specific concerning scenarios, quantify certain measures situations or entities relationships and changes in behaviour Reporting Monitoring AlertingCopyright © 2013 , SAS Institute Inc. All rights reserved.
  • 15. ANALYTICS IN OPERATIONAL ANALYTICS IN PRACTICE COMBATTING FINANCIAL CRIME FINANCIAL TRANSACTIONS Online transfer out of $10,000 What looks more like a case of Financial Crime?FINANCIALTRANSACTIONS Online transfer of $10,000, unusual for that customerNON-FINANCIALTRANSACTIONS A couple errant Access from Change of Set up of new password entries different device Account Details transfer account Increasing levels of Data to Analyse Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 16. ANALYTICS USE SOCIAL NETWORK ANALYSIS TO UNDERSTAND APPLIED UNKNOWN RELATIONSHIPs Understanding relationships and links between customers, employees, application information, device information, etc. Using systems and processing of lots of data to identify linkages that were otherwise often missed or manually developed Helps to quickly identify patterns of attempted fraud and understand potential organised crime 16Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 17. ANALYTICS BEHAVIORAL ANALYSIS AND INSIGHT DRIVES BUSINESS APPLIED VALUE Data and Information ANALYSIS Behavioural Insight Dark Side White Side Fraud Risk Marketing Reduced fraud losses More timely Credit Risk Scores More Relevant Offers More fraud prevented & Reduced Credit Risk Losses Better Targeted ProductsBusiness Benefits detected in real time Sharper Product Pricing Higher product approval rates Lower customer annoyance Better Targeted offers More higher quality and timely Lower false positives Reduction in collections cases marketing leads Better coverage against Increased customer satisfaction emerging threats Increased product retention SAS solutions can analyse behaviour across both monetary & non-monetary transactions Copyright © 2013 , SAS Institute Inc. All rights reserved.
  • 18. SAS ENTERPRISE SAS FINANCIAL CRIMES SUITEFINANCIAL CRIMES Suite approach with defined capability modules addressing Customer Due Diligence and new modules Fraud, Compliance & Security SAS Visual Analytics and BI SAS Enterprise Case Management Solutions can be consumed independently Leverages Enterprise Grade SAS Anti- SAS Fraud SAS Fraud Capabilities Money Network Management Laundering Analysis Contextual user interfaces Just received Highest rating from SAS Enterprise Financial Crimes Suite Forrester Wave Report, Feb ‘13 SAS Business Analytics FrameworkCopyright © 2013 , SAS Institute Inc. All rights reserved.
  • 19. SAS ENTERPRISE FINANCIAL CRIMES SAS FINANCIAL CRIMES SUITE – BUSINESS ARCHITECTURE Payment Currency Customer Due Diligence / Compliance Sanctions Transaction Screening/ AML / CTF FATCA Blocking Reporting Watch List Other (Telco, Banking 1st party/bust-out Health Care Account Government Insurance Credit card/debit Online/E-channel Fraud Payments fraud Utilities) fraud takeover card fraud /Transaction Security/ Application Rogue Trading Internal Fraud Insider Trading Cyber Intrusion Fraud Fraud Business Modules / IP Foundry SAS® High-Performance Analytics Security Intelligence Detection & Alert Text Mining, Foundation Generation Rule/Analyti SMA, Integrated Link c Authoring Content Triage & Workflow Real time and & Admin. Categorizati ECM Analysis batch on SAS Platform Data Management Search Analytics Dashboards & ReportingCopyright© ©0 1 2 , S A S ISAS t e I n c . A l l r i g Inc.es er v erights reserved. C op yr i g h t 2 2013, n s t i t u Institute h t s r All d . COMMERCIAL IN CONFIDENCE – for BBL Use Only
  • 20. COMMONWEALTH BANK OF BUSINESS BENEFITS FROM BIG DATA AUSTRALIACHALLENGES Analytics in Action• Stop fraudulent transactions in real time Reliably predicts the• Identify suspicious activity that requires submission of a SAR likelihood of fraud• Streamline siloed, product-specific fraud detection platforms activity for any given transaction before it isSOLUTION authorized, at the ® ®SAS Fraud Management and SAS Anti-Money Laundering average of 80-85• Real-time processing for debit cards & actively adding more channels transactions per second with a mean response• Hundreds of millions transactions analysed for money-laundering time of 40 milliseconds. detection• Behavioral analytics and models applied Analyses of up to 420 million transactions• Application fraud and internal fraud also addressed every night, looking forJOURNEY FORWARD fraud and money laundering activity.• Bank turning ‘enterprise fraud’ into reality as more channels actively being added, reducing the number of fraud systems along the way “We can do more – I have no doubt of that. While our primary role is to ensure the fraud detection systems are optimized and applicable to the threats we face, we should take every opportunity to leverage our investment in advanced systems to improve our return on investment.” – John Geurts, Chief Security Officer, CBACopyright © 2013 , SAS Institute Inc. All rights reserved.
  • 21. HSBC BUSINESS BENEFITS FROM BIG DATA CHALLENGES Analytics in Action HSBC was faced with implementing multiple scoring 87% increase in number engines for fraud and credit. They also wanted a more of data items processed while seeing 12% contemporary approach to fraud detection that could decrease in mainframe utilize new data sources such as mobile devices and processing overhead web data in their solution 30 percent decrease in SOLUTION computing resource costs for processing card SAS® Fraud Management transactions flagged as potentially fraudulent HSBC and SAS designed a new technical infrastructure that could score any type of model in real time for 100% A 10 percent increase in of transactions. This solution would allow HSBC to efficiency by agents investigating potentially conduct champion challenger, simulation of new fraudulent cases when models, integrated reporting, and a “state vector” compared to the prior concept that would allow any type of data to be used proprietary case management system. Development partner for SAS Fraud Management "SAS is committed to ensuring that we continue to have a leading-edge anti-fraud solution. We are very pleased with the results. Our IT guys like it, the business guys like it and the finance guys like it as well. Fraud analytics can often bring significant benefits, and thats certainly been our experience with SAS.“ – Derek Wylde, HSBCCopyright © 2013 , SAS Institute Inc. All rights reserved.
  • 22. BIG DATA AND BIG SUMMARY ANALYTICS FOR FRAUD Combatting Financial Crime is a great applied business Problem for Big Data, moreso Big & Operational Analytics – speed, volume and financial impact Analytics finds more financial crime - Analytics helps make sense of the vast amounts of data - turning volume, velocity variety into Vision, Veracity and Value Focus on changes in Behaviour best made possible through Big Data of Financial and Non-financial transactions SAS Solutions leverage best in class analytics, enterprise class capabilities with customer proofpoints and analyst rankingsCopyright © 2013 , SAS Institute Inc. All rights reserved.
  • 23. Keith SwansonKeith.Swanson@sas.com C o p y r i g h t © 2 0 1 3 , S A S I n s t i t u t e I Copyright l© 2010,gSAS s r e s Inc. v e rights reserved. n c . A l r i h t Institute e r All d .
  • 24. Day in the life of… Insurance Claims HandlerOld ApproachVery limited ‘red flag’ As workload permits, Very little claims to Manual review ofbusiness rules in adhoc potential fraud investigate And no information – claimClaims system identify claims received from information looking at looks OK and paymentlimited number of assessors, investigated customer, claims and vehicle to customer proceeds assuspicious claims one by one using data only history across brands scheduledNew ApproachLog on in the morning Quickly triage the Customer flagged for Further review identifies High potential for fraud,to see prioritised list of alerts, having all excessive claims same smash repair investigator opens casesuspicious claims information needed history across brands. shop used in all claims, of which workflow flagsincluding clear view of presented within one Network diagram and injured occupant call to customer andwhy claim was flagged set of screens shows VIN involved in 3 have previous claim at routes to SIU for repair accidents in 12 mths same repair shop shop visit Copyright © 2013 , SAS Institute Inc. All rights reserved.
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